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However, designs which are optimal for model discrimination may be inadequate for parameter estimation.
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We investigated whether adding trends improves accuracy and which methods are optimal for modelling trends.
The model is able to model small-scale variations in urban areas, but is not optimal for modeling dispersion along highways, so our results may not be generalizable to near-highway applications.
The optimal number of hidden neurons found to be optimal for this model was 11 (as shown in Fig. 9).
Aside from this possibility, we have analytically identified which other strategies could possibly be optimal for each model.
Root-mean-square error after cross-validation indicated that two principal components were optimal for this model.
Infusion over about 5 min was optimal for kinetic modelling with [18F]FDG in mouse brain.
Hence, optimal dimension for models with MDL and Dragon descriptors was set equal to 35 *35 neurons.
Locally and Bayesian optimal designs for models with a range of link functions are considered.
The paper presents a methodology for optimal input design for model discrimination from experimental data.
The paper presents a methodology for an optimal input design for model discrimination.
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